/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 * SimpleMI.java
 * Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
 */

package weka.classifiers.mi;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.classifiers.SingleClassifierEnhancer;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.MultiInstanceCapabilitiesHandler;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.Utils;

/**
 * <!-- globalinfo-start --> Reduces MI data into mono-instance data.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -M [1|2|3]
 *  The method used in transformation:
 *  1.arithmatic average; 2.geometric centor;
 *  3.using minimax combined features of a bag (default: 1)
 * 
 *  Method 3:
 *  Define s to be the vector of the coordinate-wise maxima
 *  and minima of X, ie., 
 *  s(X)=(minx1, ..., minxm, maxx1, ...,maxxm), transform
 *  the exemplars into mono-instance which contains attributes
 *  s(X)
 * </pre>
 * 
 * <pre>
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <pre>
 * -W
 *  Full name of base classifier.
 *  (default: weka.classifiers.rules.ZeroR)
 * </pre>
 * 
 * <pre>
 * Options specific to classifier weka.classifiers.rules.ZeroR:
 * </pre>
 * 
 * <pre>
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @author Xin Xu (xx5@cs.waikato.ac.nz)
 * @author Lin Dong (ld21@cs.waikato.ac.nz)
 * @version $Revision$
 */
public class SimpleMI extends SingleClassifierEnhancer implements
  OptionHandler, MultiInstanceCapabilitiesHandler {

  /** for serialization */
  static final long serialVersionUID = 9137795893666592662L;

  /** arithmetic average */
  public static final int TRANSFORMMETHOD_ARITHMETIC = 1;
  /** geometric average */
  public static final int TRANSFORMMETHOD_GEOMETRIC = 2;
  /** using minimax combined features of a bag */
  public static final int TRANSFORMMETHOD_MINIMAX = 3;
  /** the transformation methods */
  public static final Tag[] TAGS_TRANSFORMMETHOD = {
    new Tag(TRANSFORMMETHOD_ARITHMETIC, "arithmetic average"),
    new Tag(TRANSFORMMETHOD_GEOMETRIC, "geometric average"),
    new Tag(TRANSFORMMETHOD_MINIMAX, "using minimax combined features of a bag") };

  /** the method used in transformation */
  protected int m_TransformMethod = TRANSFORMMETHOD_ARITHMETIC;

  /**
   * Returns a string describing this filter
   * 
   * @return a description of the filter suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String globalInfo() {
    return "Reduces MI data into mono-instance data.";
  }

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   */
  @Override
  public Enumeration<Option> listOptions() {

    Vector<Option> result = new Vector<Option>();

    result.addElement(new Option("\tThe method used in transformation:\n"
      + "\t1.arithmatic average; 2.geometric centor;\n"
      + "\t3.using minimax combined features of a bag (default: 1)\n\n"
      + "\tMethod 3:\n"
      + "\tDefine s to be the vector of the coordinate-wise maxima\n"
      + "\tand minima of X, ie., \n"
      + "\ts(X)=(minx1, ..., minxm, maxx1, ...,maxxm), transform\n"
      + "\tthe exemplars into mono-instance which contains attributes\n"
      + "\ts(X)", "M", 1, "-M [1|2|3]"));

    result.addAll(Collections.list(super.listOptions()));

    return result.elements();
  }

  /**
   * Parses a given list of options.
   * <p/>
   * 
   * <!-- options-start --> Valid options are:
   * <p/>
   * 
   * <pre>
   * -M [1|2|3]
   *  The method used in transformation:
   *  1.arithmatic average; 2.geometric centor;
   *  3.using minimax combined features of a bag (default: 1)
   * 
   *  Method 3:
   *  Define s to be the vector of the coordinate-wise maxima
   *  and minima of X, ie., 
   *  s(X)=(minx1, ..., minxm, maxx1, ...,maxxm), transform
   *  the exemplars into mono-instance which contains attributes
   *  s(X)
   * </pre>
   * 
   * <pre>
   * -W
   *  Full name of base classifier.
   *  (default: weka.classifiers.rules.ZeroR)
   * </pre>
   * 
   * <pre>
   * Options specific to classifier weka.classifiers.rules.ZeroR:
   * </pre>
   * 
   * <!-- options-end -->
   * 
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  @Override
  public void setOptions(String[] options) throws Exception {

    String methodString = Utils.getOption('M', options);
    if (methodString.length() != 0) {
      setTransformMethod(new SelectedTag(Integer.parseInt(methodString),
        TAGS_TRANSFORMMETHOD));
    } else {
      setTransformMethod(new SelectedTag(TRANSFORMMETHOD_ARITHMETIC,
        TAGS_TRANSFORMMETHOD));
    }

    super.setOptions(options);

    Utils.checkForRemainingOptions(options);
  }

  /**
   * Gets the current settings of the Classifier.
   * 
   * @return an array of strings suitable for passing to setOptions
   */
  @Override
  public String[] getOptions() {

    Vector<String> result = new Vector<String>();

    result.add("-M");
    result.add("" + m_TransformMethod);

    Collections.addAll(result, super.getOptions());

    return result.toArray(new String[result.size()]);
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String transformMethodTipText() {
    return "The method used in transformation.";
  }

  /**
   * Set the method used in transformation.
   * 
   * @param newMethod the index of method to use.
   */
  public void setTransformMethod(SelectedTag newMethod) {
    if (newMethod.getTags() == TAGS_TRANSFORMMETHOD) {
      m_TransformMethod = newMethod.getSelectedTag().getID();
    }
  }

  /**
   * Get the method used in transformation.
   * 
   * @return the index of method used.
   */
  public SelectedTag getTransformMethod() {
    return new SelectedTag(m_TransformMethod, TAGS_TRANSFORMMETHOD);
  }

  /**
   * Implements MITransform (3 type of transformation) 1.arithmatic average;
   * 2.geometric centor; 3.merge minima and maxima attribute value together
   * 
   * @param train the multi-instance dataset (with relational attribute)
   * @return the transformed dataset with each bag contain mono-instance
   *         (without relational attribute) so that any classifier not for MI
   *         dataset can be applied on it.
   * @throws Exception if the transformation fails
   */
  public Instances transform(Instances train) throws Exception {

    Attribute classAttribute = (Attribute) train.classAttribute().copy();
    Attribute bagLabel = train.attribute(0);
    double labelValue;

    Instances newData = train.attribute(1).relation().stringFreeStructure();

    // insert a bag label attribute at the begining
    newData.insertAttributeAt(bagLabel, 0);

    // insert a class attribute at the end
    newData.insertAttributeAt(classAttribute, newData.numAttributes());
    newData.setClassIndex(newData.numAttributes() - 1);

    Instances mini_data = newData.stringFreeStructure();
    Instances max_data = newData.stringFreeStructure();

    Instance newInst = new DenseInstance(newData.numAttributes());
    Instance mini_Inst = new DenseInstance(mini_data.numAttributes());
    Instance max_Inst = new DenseInstance(max_data.numAttributes());
    newInst.setDataset(newData);
    mini_Inst.setDataset(mini_data);
    max_Inst.setDataset(max_data);

    double N = train.numInstances();// number of bags
    for (int i = 0; i < N; i++) {
      int attIdx = 1;
      Instance bag = train.instance(i); // retrieve the bag instance
      labelValue = bag.value(0);
      if (m_TransformMethod != TRANSFORMMETHOD_MINIMAX) {
        newInst.setValue(0, labelValue);
      } else {
        mini_Inst.setValue(0, labelValue);
        max_Inst.setValue(0, labelValue);
      }

      Instances data = bag.relationalValue(1); // retrieve relational value for
                                               // each bag
      for (int j = 0; j < data.numAttributes(); j++) {
        double value;
        if (m_TransformMethod == TRANSFORMMETHOD_ARITHMETIC) {
          value = data.meanOrMode(j);
          newInst.setValue(attIdx++, value);
        } else if (m_TransformMethod == TRANSFORMMETHOD_GEOMETRIC) {
          double[] minimax = minimax(data, j);
          value = (minimax[0] + minimax[1]) / 2.0;
          newInst.setValue(attIdx++, value);
        } else { // m_TransformMethod == TRANSFORMMETHOD_MINIMAX
          double[] minimax = minimax(data, j);
          mini_Inst.setValue(attIdx, minimax[0]);// minima value
          max_Inst.setValue(attIdx, minimax[1]);// maxima value
          attIdx++;
        }
      }

      if (m_TransformMethod == TRANSFORMMETHOD_MINIMAX) {
        if (!bag.classIsMissing()) {
          max_Inst.setClassValue(bag.classValue()); // set class value
        }
        mini_data.add(mini_Inst);
        max_data.add(max_Inst);
      } else {
        if (!bag.classIsMissing()) {
          newInst.setClassValue(bag.classValue()); // set class value
        }
        newData.add(newInst);
      }
    }

    if (m_TransformMethod == TRANSFORMMETHOD_MINIMAX) {
      mini_data.setClassIndex(-1);
      mini_data.deleteAttributeAt(mini_data.numAttributes() - 1); // delete
                                                                  // class
                                                                  // attribute
                                                                  // for the
                                                                  // minima data
      max_data.deleteAttributeAt(0); // delete the bag label attribute for the
                                     // maxima data

      newData = Instances.mergeInstances(mini_data, max_data); // merge minima
                                                               // and maxima
                                                               // data
      newData.setClassIndex(newData.numAttributes() - 1);

    }

    return newData;
  }

  /**
   * Get the minimal and maximal value of a certain attribute in a certain data
   * 
   * @param data the data
   * @param attIndex the index of the attribute
   * @return the double array containing in entry 0 for min and 1 for max.
   */
  public static double[] minimax(Instances data, int attIndex) {
    double[] rt = { Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY };
    for (int i = 0; i < data.numInstances(); i++) {
      double val = data.instance(i).value(attIndex);
      if (val > rt[1]) {
        rt[1] = val;
      }
      if (val < rt[0]) {
        rt[0] = val;
      }
    }

    for (int j = 0; j < 2; j++) {
      if (Double.isInfinite(rt[j])) {
        rt[j] = Double.NaN;
      }
    }

    return rt;
  }

  /**
   * Returns default capabilities of the classifier.
   * 
   * @return the capabilities of this classifier
   */
  @Override
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.RELATIONAL_ATTRIBUTES);
    result.disable(Capability.MISSING_VALUES);

    // class
    result.disableAllClasses();
    result.disableAllClassDependencies();
    if (super.getCapabilities().handles(Capability.NOMINAL_CLASS)) {
      result.enable(Capability.NOMINAL_CLASS);
    }
    if (super.getCapabilities().handles(Capability.BINARY_CLASS)) {
      result.enable(Capability.BINARY_CLASS);
    }
    result.enable(Capability.MISSING_CLASS_VALUES);

    // other
    result.enable(Capability.ONLY_MULTIINSTANCE);

    return result;
  }

  /**
   * Returns the capabilities of this multi-instance classifier for the
   * relational data.
   * 
   * @return the capabilities of this object
   * @see Capabilities
   */
  @Override
  public Capabilities getMultiInstanceCapabilities() {
    Capabilities result = super.getCapabilities();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    // class
    result.disableAllClasses();
    result.enable(Capability.NO_CLASS);

    return result;
  }

  /**
   * Builds the classifier
   * 
   * @param train the training data to be used for generating the boosted
   *          classifier.
   * @throws Exception if the classifier could not be built successfully
   */
  @Override
  public void buildClassifier(Instances train) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(train);

    // remove instances with missing class
    train = new Instances(train);
    train.deleteWithMissingClass();

    if (m_Classifier == null) {
      throw new Exception("A base classifier has not been specified!");
    }

    if (getDebug()) {
      System.out.println("Start training ...");
    }
    Instances data = transform(train);

    data.deleteAttributeAt(0); // delete the bagID attribute
    m_Classifier.buildClassifier(data);

    if (getDebug()) {
      System.out.println("Finish building model");
    }
  }

  /**
   * Computes the distribution for a given exemplar
   * 
   * @param newBag the exemplar for which distribution is computed
   * @return the distribution
   * @throws Exception if the distribution can't be computed successfully
   */
  @Override
  public double[] distributionForInstance(Instance newBag) throws Exception {

    double[] distribution = new double[2];
    Instances test = new Instances(newBag.dataset(), 0);
    test.add(newBag);

    test = transform(test);
    test.deleteAttributeAt(0);
    Instance newInst = test.firstInstance();

    distribution = m_Classifier.distributionForInstance(newInst);

    return distribution;
  }

  /**
   * Gets a string describing the classifier.
   * 
   * @return a string describing the classifer built.
   */
  @Override
  public String toString() {
    return "SimpleMI with base classifier: \n" + m_Classifier.toString();
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision$");
  }

  /**
   * Main method for testing this class.
   * 
   * @param argv should contain the command line arguments to the scheme (see
   *          Evaluation)
   */
  public static void main(String[] argv) {
    runClassifier(new SimpleMI(), argv);
  }
}
